from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
 import torch


a  :      List[str]            =    logging.get_logger(__name__)

class 					_lowercase    (     snake_case_			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Union[str, Any]          =    ["""pixel_values"""]




    def __init__(    self       ,						lowerCamelCase__ = True       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = PILImageResampling.BILINEAR       ,						lowerCamelCase__ = True       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = True       ,						lowerCamelCase__ = 1 / 255       ,						lowerCamelCase__ = True       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = None       ,						**lowerCamelCase__       ,						):
       super().__init__(**lowerCamelCase__ )
       lowerCAmelCase_:							int       =	size if size is not None else {"shortest_edge": 256}
       lowerCAmelCase_:							Union[str, Any]       =	get_size_dict(lowerCamelCase__       ,						default_to_square=lowerCamelCase__ )
       lowerCAmelCase_:							List[str]       =	crop_size if crop_size is not None else {"height": 224, "width": 224}
       lowerCAmelCase_:							Dict       =	get_size_dict(lowerCamelCase__       ,						param_name="crop_size" )
       lowerCAmelCase_:							Tuple       =	do_resize
       lowerCAmelCase_:							Union[str, Any]       =	size
       lowerCAmelCase_:							int       =	resample
       lowerCAmelCase_:							int       =	do_center_crop
       lowerCAmelCase_:							str       =	crop_size
       lowerCAmelCase_:							Optional[int]       =	do_rescale
       lowerCAmelCase_:							int       =	rescale_factor
       lowerCAmelCase_:							Optional[Any]       =	do_normalize
       lowerCAmelCase_:							Union[str, Any]       =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       lowerCAmelCase_:							Dict       =	image_std if image_std is not None else IMAGENET_STANDARD_STD




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ = PILImageResampling.BICUBIC       ,						lowerCamelCase__ = None       ,						**lowerCamelCase__       ,						):
       lowerCAmelCase_:							Any       =	get_size_dict(lowerCamelCase__       ,						default_to_square=lowerCamelCase__ )
       if "shortest_edge" not in size:
          raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
       lowerCAmelCase_:							int       =	get_resize_output_image_size(lowerCamelCase__       ,						size=size["shortest_edge"]       ,						default_to_square=lowerCamelCase__ )
       return resize(lowerCamelCase__       ,						size=lowerCamelCase__       ,						resample=lowerCamelCase__       ,						data_format=lowerCamelCase__       ,						**lowerCamelCase__ )




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ = None       ,						**lowerCamelCase__       ,						):
       lowerCAmelCase_:							str       =	get_size_dict(lowerCamelCase__ )
       if "height" not in size or "width" not in size:
          raise ValueError(F'''The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}''' )
       return center_crop(lowerCamelCase__       ,						size=(size["height"], size["width"])       ,						data_format=lowerCamelCase__       ,						**lowerCamelCase__ )




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ = None       ,						**lowerCamelCase__ ):
       return rescale(lowerCamelCase__       ,						scale=lowerCamelCase__       ,						data_format=lowerCamelCase__       ,						**lowerCamelCase__ )




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ = None       ,						**lowerCamelCase__       ,						):
       return normalize(lowerCamelCase__       ,						mean=lowerCamelCase__       ,						std=lowerCamelCase__       ,						data_format=lowerCamelCase__       ,						**lowerCamelCase__ )




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = ChannelDimension.FIRST       ,						**lowerCamelCase__       ,						):
       lowerCAmelCase_:							List[str]       =	do_resize if do_resize is not None else self.do_resize
       lowerCAmelCase_:							Optional[Any]       =	size if size is not None else self.size
       lowerCAmelCase_:							List[str]       =	get_size_dict(lowerCamelCase__       ,						default_to_square=lowerCamelCase__ )
       lowerCAmelCase_:							str       =	resample if resample is not None else self.resample
       lowerCAmelCase_:							List[str]       =	do_center_crop if do_center_crop is not None else self.do_center_crop
       lowerCAmelCase_:							Optional[Any]       =	crop_size if crop_size is not None else self.crop_size
       lowerCAmelCase_:							List[str]       =	get_size_dict(lowerCamelCase__       ,						param_name="crop_size" )
       lowerCAmelCase_:							List[Any]       =	do_rescale if do_rescale is not None else self.do_rescale
       lowerCAmelCase_:							Optional[int]       =	rescale_factor if rescale_factor is not None else self.rescale_factor
       lowerCAmelCase_:							List[Any]       =	do_normalize if do_normalize is not None else self.do_normalize
       lowerCAmelCase_:							Optional[int]       =	image_mean if image_mean is not None else self.image_mean
       lowerCAmelCase_:							Dict       =	image_std if image_std is not None else self.image_std

       lowerCAmelCase_:							Union[str, Any]       =	make_list_of_images(lowerCamelCase__ )

       if not valid_images(lowerCamelCase__ ):
          raise ValueError(
              "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
              "torch.Tensor, tf.Tensor or jax.ndarray." )

       if do_resize and size is None:
          raise ValueError("Size must be specified if do_resize is True." )

       if do_center_crop and crop_size is None:
          raise ValueError("Crop size must be specified if do_center_crop is True." )

       if do_rescale and rescale_factor is None:
          raise ValueError("Rescale factor must be specified if do_rescale is True." )

       if do_normalize and (image_mean is None or image_std is None):
          raise ValueError("Image mean and std must be specified if do_normalize is True." )

       # All transformations expect numpy arrays.
       lowerCAmelCase_:							Optional[int]       =	[to_numpy_array(lowerCamelCase__ ) for image in images]

       if do_resize:
          lowerCAmelCase_:							Optional[int]       =	[self.resize(image=lowerCamelCase__       ,						size=lowerCamelCase__       ,						resample=lowerCamelCase__ ) for image in images]

       if do_center_crop:
          lowerCAmelCase_:							List[str]       =	[self.center_crop(image=lowerCamelCase__       ,						size=lowerCamelCase__ ) for image in images]

       if do_rescale:
          lowerCAmelCase_:							Dict       =	[self.rescale(image=lowerCamelCase__       ,						scale=lowerCamelCase__ ) for image in images]

       if do_normalize:
          lowerCAmelCase_:							Optional[Any]       =	[self.normalize(image=lowerCamelCase__       ,						mean=lowerCamelCase__       ,						std=lowerCamelCase__ ) for image in images]

       lowerCAmelCase_:							Tuple       =	[to_channel_dimension_format(lowerCamelCase__       ,						lowerCamelCase__ ) for image in images]

       lowerCAmelCase_:							int       =	{"pixel_values": images}
       return BatchFeature(data=lowerCamelCase__       ,						tensor_type=lowerCamelCase__ )




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__ = None ):
       lowerCAmelCase_:							List[Any]       =	outputs.logits

       # Resize logits and compute semantic segmentation maps
       if target_sizes is not None:
          if len(lowerCamelCase__ ) != len(lowerCamelCase__ ):
             raise ValueError(
                 "Make sure that you pass in as many target sizes as the batch dimension of the logits" )

          if is_torch_tensor(lowerCamelCase__ ):
             lowerCAmelCase_:							Optional[Any]       =	target_sizes.numpy()

          lowerCAmelCase_:							int       =	[]

          for idx in range(len(lowerCamelCase__ ) ):
             lowerCAmelCase_:							Optional[int]       =	torch.nn.functional.interpolate(
                 logits[idx].unsqueeze(dim=0 )       ,						size=target_sizes[idx]       ,						mode="bilinear"       ,						align_corners=lowerCamelCase__ )
             lowerCAmelCase_:							int       =	resized_logits[0].argmax(dim=0 )
             semantic_segmentation.append(lowerCamelCase__ )
       else:
          lowerCAmelCase_:							List[str]       =	logits.argmax(dim=1 )
          lowerCAmelCase_:							List[str]       =	[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]

       return semantic_segmentation
"""simple docstring"""



def 	_lowerCamelCase(	a				):
       return " ".join(
           "".join(word[::-1]				) if len(a				) > 4 else word for word in sentence.split()				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(reverse_long_words("""Hey wollef sroirraw"""))
